Two conventional computer models have become well known in the industry of computing. The first is a mainframe computing model and the second is a clustered computing model.
The traditional progression for an end user in the mainframe computing model is to purchase an initial system, and when additional processing capabilities are required, to replace the initial system with a bigger system. At various points in this cycle, traumatic discontinuities occur. For example, if the user outgrows the architecture of the initial system, the user may need to convert from one operating system to another, or even from one vendor's proprietary architecture to that of another vendor, when the second upgraded mainframe system is purchased. These changes entail enormous costs for the organization purchasing the upgrade, in both dollars and employee time. Therefore, such conversions are avoided, in many cases.
In addition, the mainframe model entails poor residual value of computer equipment. Thus, the system replacement often results in invested capital which is substantially completely lost when the initial system is replaced by an upgraded system. Further, larger upgraded systems tend to be sold in lower volumes then smaller systems. Thus, each new system upgrade typically has a higher cost of computing than the previous system.
In a clustered computing model, a mainframe computer is replaced with a cluster of smaller, standards-based servers. This can offer many advantages over the mainframe model. Since the cluster may start off as only a single system, the threshold to entering the cluster model is lower. Further, such smaller systems are typically sold in high volume, making the cost of computing less. Also, such systems are standards based in that they do not exhibit dependence on proprietary architectures. This provides for the availability of equipment from multiple sources which allows the user to choose the best alternative with each subsequent purchase.
Still other advantages present themselves with the clustered computing model. Upgrade costs can be controlled more precisely by adding only the amount of additional resources required to meet existing and immediate future needs. Further, the user can choose from a wide variety of vendors, without concern about migration or conversion to a new architecture. Similarly, with the right architecture, there may never be a need for conversion to another operating system.
Still, the clustered computing model does have disadvantages and problems. For example, the clustered computing model encounters difficulty in providing clustered systems with the ability to share data in a way that allows the cluster to take on the workload that a single mainframe could perform. For example, it is currently very difficult to implement clustered models where each of the servers in the cluster are required to process transactions on the same data. Examples of some such applications include an airlines reservations system or a financial institution's complete inventory of transactions.
The second disadvantage of the clustered computing model simply involves the lack of extensive experience in managing storage and data which exists in the mainframe environment. Such experience has evolved into management software that is simply not yet available in the standards based cluster environment.
The present invention addresses these and other problems, and offers other advantages over the prior art.